Schedule for English 238 (Fall 2021)
Digital Humanities: Introduction to the Field
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A Note About Access to Reading Materials For This Course (See also Guide to Downloading and Organizing Online Readings) |
 = PDF  = Video = Paywalled (off-campus access through UCSB VPN) = Course password required |
Introduction (Renegotiating “Liberal Arts” and “Human Sciences” — Humanities, Digital Humanities, and Data Science)
- Sister Miriam Joseph, “What Are the Liberal Arts?” (2002)
- Stephen P. Turner, “Human Sciences, History Of” (2015)
- Rens Bod, “Introduction: The Quest for Principles and Patterns” (Chap. 1 of New History of the Humanities, 2013) (course password required)
- Browse the following sites representative of current “data science” higher-education initiatives:
- Matthew G. Kirschenbaum, “What Is Digital Humanities and What’s It Doing in English Departments?” (2010/2012)
- Alan Liu, “Is Digital Humanities a Field? ‒ An Answer from the Point of View of Language” (2016)
Data (and/as Idea, Form, Structure, Model, Prediction)
- Daniel Rosenberg, “Data Before the Fact,” in Lisa Gitelman, ed., “Raw Data” Is an Oxymoron (2013), pp. 15-40
- Johanna Drucker, “Humanities Approaches to Graphical Display” (2011) — read only paragraphs 1-30 (subsequent paragraphs are assigned for later in the course)
- Yin Liu, “Ways of Reading, Models for Text, and the Usefulness of Dead People” (2013)
- Caroline Levine, “Introduction: The Affordances of Form,” in Forms: Whole, Rhythm, Hierarchy, Network (2015) (course password required)
- Claude Lévi-Strauss, “The Structural Study of Myth” (1955). — read only pp. 431-436 (para. 2.6-5.3)
- Willard C. McCarty, “Modeling: A study in Words and Meanings” (2004)
- Matthew L. Jones, “How We Became Instrumentalists (Again): Data Positivism since World War II” (2018)
Archives (in the Digital and Big Data Age)
- Luciana Duranti, “Archives as a Place” (1996)
- For knformation about Sir Hilary Jenkinson, mentioned by Duranti, see Michael Piggott, “Alchemist Magpies? Collecting Archivists and Their Criticms” and the section on “archival theory” in the Wikipedia article on Jenkinson.
- Jefferson Bailey, “Disrespect des Fonds: Rethinking Arrangement and Description in Born-Digital Archives” (2013)
- For help on the idea of an archival “series” in relation to the older notion of provenance according to respect des fonds, see the U.S. National Archives’ explanation of how series fit in “record groups” in its organization of records.
- Kim Gallon, “Making a Case for the Black Digital Humanities” (2016)
- Jessica Marie Johnson, “Markup Bodies: Black [Life] Studies and Slavery [Death] Studies at the Digital Crossroads” (2018)
- As accompaniment to Jessica Marie Johnson’s essay, explore Slave Voyages (including the Trans-Atlantic Slave Trade Database)
- Jo Guldi and David Armitage, “Big Questions, Big Data,” in The History Manifesto (2014), pp. 88-116
- Jonathan Stuart Ward and Adam Barker, “Undefined By Data: A Survey of Big Data Definitions” (2013)
Text Encoding (& its Discontents)
- Yin Liu, “Ways of Reading, Models for Text, and the Usefulness of Dead People” (2013) — quickly review this essay assigned for a previous class
- William Warner, Kimberly Knight, and UCSB Transliteracies History of Reading Group, “In the Beginning was the Word: A Visualization of the Page as Interface” — The original, interactive Flash animation is not viewable because the Flash program is no longer supported in browsers and operating systems. View instead the MP4 video recording of a playthrough of the original Flash.
- Steven J. DeRose, David G. Durand, Elli Mylonas, and Allen H. Renear, “What Is Text, Really?” (1990)
- Allen H. Renear, Elli Mylonas, and David G. Durand, “Refining Our Notion of What Text Really Is” (1993)
- Susan Hockey, Allen Renear, and Jerome J. McGann, “What Is Text? A Debate on the Philosophical and Epistemological Nature of Text in the Light of Humanities Computing Research” (1999)
- Kate Singer, “Digital Close Reading: TEI for Teaching Poetic Vocabularies” (2013)
- [Optional: look at the poem and site associated with the course discussed in Kate Singer’s essay: Melesina Trench Edition | “Laura’s Dream, or The Moonlanders” (1816)]
- Alan Liu, “Transcendental Data: Toward a Cultural History and Aesthetics of the New Encoded Discourse” (2004)
Text Analysis (& its Discontents) — “Distant Reading”
- Franco Moretti, “Graphs,” in Graphs, Maps, Trees: Abstract Models for a Literary History (2007), pp. 1-33 (course password required)
- Ryan Heuser and Long Le-Khac, “A Quantitative Literary History of 2,958 Nineteenth-Century British Novels: The Semantic Cohort Method” (2012)
- Alan Liu, “The Meaning of the Digital Humanities” (2012)
- Richard Jean So and Edwin Roland, “Race and Distant Reading” (2020)
- Lauren Klein and Sandeep Soni, “How Words Lead to Justice” (2021)
- Rob Kitchin, The Data Revolution: Big Data, Open Data, Data Infrastructures and Their Consequences (2014), chap. 8 “The Reframing of Science, Social Science and Humanities Research” (pp. 128-148), and pp. 158-160 (course password required)
- Nan Z. Da, “The Computational Case against Computational Literary Studies” (2019)
Text Analysis (continued) — Topic Modeling
- Megan Dibble, “An Explanation of Machine Learning Models Even You Could Understand” (2020)
- David M. Blei, “Probabilistic Topic Models” (2013)
— read only to end of p. 79, before the math begins - Ted Underwood, “Topic Modeling Made Just Simple Enough” (2012)
- John Mohr and Petko Bogdanov, “Introduction — Topic Models: What They Are and Why They Matter” (2013)
- Andrew Piper, Excerpt from “Topoi (Dispersion),” in Enumerations: Data and Literary Study (2019) — read only pp. 66–75 (course password required)
- Examples of Topic Models & Model Visualizations:
- Andrew Goldstone’s interface for exploring topic models. The Signs model has some extra, later-developed features. Especially helpful in learning how to work with these models is the guide page on “Interpreting the topic model of Signs“
- WhatEvery1Says (WE1S) Project, U.S. News Media, c. 1989-2019, Collection 1 — click on options under “Topic Models of This Collection”
Social Network Analysis
- Stephen P. Borgatti, et al., “Network Analysis in the Social Sciences” (2009)
- Elijah Meeks and Scott B. Weingart, “Introduction to Network Analysis and Representation” — click on the tabs for “centrality, ” “clustering coefficient,” etc. for brief interactive tutorials
- Martin Grandjean, “A Social Network Analysis of Twitter: Mapping the Digital Humanities Community” (2016)
- Optional: NodeXL network graph of Nov. 4, 2021, for Twitter posts containing “digital humanities” OR “digital humanist” (scroll down page for statistics)
- Paola Pascual-Ferrá, Neil Alperstein, and Daniel J. Barnett, “Social Network Analysis of COVID-19 Public Discourse on Twitter: Implications for Risk Communication” (2020)
- Franco Moretti, “Network Theory, Plot Analysis” (2011)
- Examples of historical social-network visualization projects:
- Christopher N. Warren et al., Six Degrees of Francis Bacon (2015)
- Optional: for a detailed explanation of methods used to create this project, see Christopher N. Warren et al., “Six Degrees of Francis Bacon: A Statistical Method for Reconstructing Large Historical Social Networks” (2016)
- Nicholas Jenkins, Elijah Meeks, and Scott Murray, “Kindred Britain” (2013)
- Optional: Nicholas Jenkins’s reflective essay on the personal and conceptual background of the project: “Originating Kindred Britain.”
- Christopher N. Warren et al., Six Degrees of Francis Bacon (2015)
The DH Reimagination of Space & Time
(Spatial and Temporal Analysis)
- GIS and Other Maps
- Ian Gregory and David Cooper, “Geographical Technologies and the Interdisciplinary Study of Peoples and Cultures of the Past” (2013)
- Ian N. Gregory, “Different Places, Different Stories: Infant Mortality Decline in England and Wales, 1851–1911” (2008)
- Barbara Piatti et al., “Mapping Literature: Towards a Geography of Fiction” (2009)
- See also the project associated with this article: A Literary Atlas of Europe
- Ian Gregory and David Cooper, “Geographical Technologies and the Interdisciplinary Study of Peoples and Cultures of the Past” (2013)
- The Past and Future of the “Timeline”
- Daniel Rosenberg and Anthony Grafton, Cartographies of Time (2010) [synopsis, quotations, and illustrations from the book from a Williams College 2011 course, “THEA 228: The Cartographic Imagination” ]
- Florian Kräutli, “Visualising Cultural Data: Exploring Digital Collections Through Timeline Visualisations” (diss. 2017), pp. 100-122 (beginning with section on “Static Timeines”) — download from “Documents” tab on the page
- Daniel Rosenberg and Anthony Grafton, Cartographies of Time (2010) [synopsis, quotations, and illustrations from the book from a Williams College 2011 course, “THEA 228: The Cartographic Imagination”
- Data and/vs. Narrative
- Lev Manovich, The Language of New Media (2001), pp. 218-28 (starts at p. 134 of the PDF file)
- Hans Rosling, “Hans Rosling’s 200 Countries, 200 Years, 4 Minutes — The Joy of Stats” (2010)
- Martha Kang, “Exploring the 7 Different Types of Data Stories” (2015)
- Lev Manovich, The Language of New Media (2001), pp. 218-28 (starts at p. 134 of the PDF file)
- Toward Imagining other Spaces & Times
- Johanna Drucker, “Humanities Approaches to Graphical Display” (2011) — read paragraphs 31-48
- Alan Liu, “Toward a Diversity Stack: Digital Humanities and Diversity as Technical Problem” (2020), pp. 141-142 — read only section on “The DH Chronotope”
AI — Neural-Network Artificial Intelligence
- Neural Networks
- Nicholas Thompson, “An AI Pioneer Explains the Evolution of Neural Networks” (2019)
- Chris Nicholson,
- Mahdi Allahyari, “Convolutional Neural Networks” (2020) (read first two sections on “Convolutional Neural Networks (CNNs / ConvNets)” and “CNNs Architecture”)
- You may also want to look at this short, infographic-style explanation of neural networks: Samuel K. Moore, et al., “How Deep Learning Works” (2021).
- Fabian Offert and Peter Bell, “Generative Digital Humanities” (2020)
- Word Embedding
- Benjamin Schmidt, “Vector Space Models for the Digital Humanities” (2015) — read only through the section on “Optimally positioning words in space,” and then browse quickly through the rest of the post. Note: this web page will appear to hang for a minute or so (at a position several screens down) as interactive visualizations are downloaded. Just wait for the page to complete.
- Ryan Heuser, “Word Vectors in the Eighteenth Century (talk abstract)” (2017) (See also sections 1-=2 of Heuser’s post on “Methods” of word vectors
- Fabian Offert, “Intuition and Epistemology of High-Dimensional Vector Space” (2021)
- Phillip Schmitt
- Large Language Models
- Demi Ajayi, “How BERT and GPT Models Change the Game for NLP” (2020)
- Adam Daniel King, InferKit Demo (2020) (interactive demo of language generation using GPT-2) — repeatedly click “Generate Text” to continue creating discourse based on previously generated discourse
- Minh Hua and Rita Raley, “Playing With Unicorns: AI Dungeon and Citizen NLP” (2020)
- Ted Underwood, “Science Fiction Hasn’t Prepared Us to Imagine Machine Learning” (2021)
- Emily M. Bender et al., “On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?” (2021)
[Project Proposals Presentations]
- Readings Serving as Coda for the Course
- Presentations of Student Project Proposals
A Note About Access to Reading Materials For This Course
All readings are online. Paywalled articles can be accessed over the UCSB network (or from off-campus by using the campus Pulse VPN service or the campus Library Proxy Server). You can also try to find open-access versions of paywalled materials using the Unpaywall extension for the Chrome or Firefox browsers. (Advice: It is a good idea to download materials as early as possible in case, for example, PDFs that are currently available open-access, on the open net, or through a UCSB Library digital database subscription later become inaccessible.)
Because so many readings are online (an increasingly prevalent trend in college courses), students will need to develop a method or workflow for themselves that optimizes their ability to study the materials. While everyone has their own personal preferences and technical constraints, the following guide includes suggested options for handling online materials:
